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In ultra-short laser pulses, small changes in dispersion properties before the final focusing mirror can lead to severe pulse distortions around the focus and therefore to very different pulse properties at the point of laser-matter interaction yielding unexpected interaction results. The mapping between far and near-field laser properties intricately depends on the spatial and angular dispersion properties as well as the focal geometry. For a focusing Gaussian laser pulse subject to angular, spatial, and group delay dispersion, we derive analytical expressions for its pulse-front tilt, duration, and width from a fully analytic expression for its electric field in time-space domain. This expression is not only valid in and near the focus but along the entire propagation distance from the focusing mirror to the focus. Together with expressions relating angular, spatial, and group delay dispersion before focusing at an off-axis parabola to the respective values in the pulse’s focus, these formulas are used to show in example setups that pulse-front tilts of lasers with small initial dispersion can become several ten degrees large in the vicinity of the focus while being small directly in the focus. The formulas derived here provide the analytical foundation for observations previously made in numerical experiments. By numerically simulating Gaussian pulse propagation and measuring properties of the pulse at distances several Rayleigh lengths off the focus we verified the analytic expressions.

Laser-driven ion accelerators can deliver high-energy, high-peak current beams and are thus attracting attention as a compact alternative to conventional accelerators. However, achieving sufficiently high energy levels suitable for applications such as radiation therapy remains a challenge for laser-driven ion accelerators. Here we generate proton beams with a spectrally separated high-energy component of up to 150 MeV by irradiating solid-density plastic foil targets with ultrashort laser pulses from a repetitive petawatt laser. The preceding laser light heats the target, leading to the onset of relativistically induced transparency upon main pulse arrival. The laser peak then penetrates the initially opaque target and triggers proton acceleration through a cascade of different mechanisms, as revealed by three-dimensional particle-in-cell simulations. The transparency of the target can be used to identify the high-performance domain, making it a suitable feedback parameter for automated laser and target optimization to enhance stability of plasma accelerators in the future.

This repository contains data on coherent optical transition radiation (COTR) from laser wakefield accelerated electron beams. This includes raw (COTR) images and electron spectra, as well as analysis code for evaluating the COTR data and using it as an input for a differential-evolution-based reconstruction of the electron bunch.

Phase retrieval is an ill-posed inverse problem with several applications in the fields of medical imaging and materials science. Conventional phase retrieval algorithms either simplify the problem by assuming certain object properties and optical propagation regimes or tuning a large number of free parameters. While the latter most often leads to good solutions for a wider application range, it is still a time-consuming process, even for experienced users. One way to circumvent this is by introducing a self-optimizing machine learning- based algorithm. Basing this on invertible networks such as normalising flows ensures good inversion, effi- cient sampling, and fast probability density estimation for large images and generally, complex-valued dis- tributions. Here, complex wavefield datasets are trained and tested on a normalising flows-based machine learning model for phase retrieval called conditional Wavelet Flow (cWF) and benchmarked against other conventional algorithms and baseline models. The cWF algorithm adds a conditioning network on top of the Wavelet Flow algorithm that is able to model the conditional data distribution of high resolution images of up to 1024 x 1024 pixels, which was not possible in other flow-based models. Additionally, cWF takes advantage of the parallelized training of different image resolutions, allowing for more efficient and fast training of large datasets. The trained algorithm is then applied to X-ray holography data wherein fast and high-quality image reconstruction is made possible.

Source data for the publication titled "Revealing the 3D structure of microbunched plasma-wakefield-accelerated electron beams."

This dataset contains all source data used to generate figures and all other findings of the publication: " Enhanced ion acceleration from transparency-driven foils demonstrated at two ultraintense laser facilities".

Phase retrieval is an ill-posed inverse problem with several applications in the fields of medical imaging and materials science. Conventional phase retrieval algorithms either simplify the problem by assuming certain object properties and optical propagation regimes or tuning a large number of free parameters. While the latter most often leads to good solutions for a wider application range, it is still a time-consuming process, even for experienced users. One way to circumvent this is by introducing a self-optimizing machine learning- based algorithm. Basing this on invertible networks such as normalising flows ensures good inversion, effi- cient sampling, and fast probability density estimation for large images and generally, complex-valued dis- tributions. Here, complex wavefield datasets are trained and tested on a normalising flows-based machine learning model for phase retrieval called conditional Wavelet Flow (cWF) and benchmarked against other conventional algorithms and baseline models. The cWF algorithm adds a conditioning network on top of the Wavelet Flow algorithm that is able to model the conditional data distribution of high resolution images of up to 1024 x 1024 pixels, which was not possible in other flow-based models. Additionally, cWF takes advantage of the parallelized training of different image resolutions, allowing for more efficient and fast training of large datasets. The trained algorithm is then applied to X-ray holography data wherein fast and high-quality image reconstruction is made possible.

This dataset contains all source data used to generate figures and all other findings of the publication: "Laser-driven high-energy proton beams from cascaded acceleration regimes".

Recently, free electron lasing at UV wavelength has been demonstrated by deploying the COXINEL beamline driven by HZDR plasma accelerator in a seeded configuration[1]. Further control and optimization of such an FEL radiation require full knowledge of strongly-coupled multivariate parameters involved in laser plasma acceleration, electron beam transport and radiation generation. For this purpose, one has to solve an inverse problem, i.e. finding matching parameters of the simulation to reproduce the experiment. Such inverse problems are ill-posed and cannot be easily resolved due to high computational complexity. Here, machine learning-based methods have a high potential to accelerate theoretical comprehension of the system, novel means for design space exploration and promise reliable in-situ analysis of experimental diagnostics and parameters. We apply simulation-based inference technique for this purpose. This method is a combination of deep learning and statistical approaches to resolve an inverse problem up to a posterior distribution of the simulation parameters given an experimental sample. In addition, we have developed machine learning-based surrogate models that can significantly accelerate forward computations for even faster results of the inverse solver. [1] M. Labat, et al. "Seeded free-electron laser in driven by a compact laser plasma accelerator", Nat. Photonics, 17, 150(2023)

The output of simulations can be extremely challenging to work with. For example, in large-scale particle-in-cell simulations in plasma physics, trillions of particles are simulated over millions of time steps, creating Petabytes of data. In this project, we develop methods to compress particle data by training conditional invertible neural networks (cINNs) on the particle data. The particles then can be reconstructed by running the trained model in generative mode. This allows us to reach up to millionfold compression, and a controlled loss of a accuracy. The models can be conditioned not only on the temporal axis but also on other types of simulation outputs of smaller data volume, leading potentially to even higher compression factors. In order to enable the neural network model to represent simulation data over long time spans, we apply methods from Continual Learning, where each new learning task is represented by a dataset produced by a simulation time step. This approach enables us to efficiently solve the inverse problem of particle data reconstruction from radiation data in a time-resolved manner by side-stepping demanding simulations.

Ion acceleration by laser-plasma sources promises many applications, but reaching the required beam quality parameters demands a high level of understanding and control over the interaction process. Several advanced schemes, including the Relativistically Induced Transparency (RIT) regime, have been proposed and investigated in search of a stable acceleration for proton energies beyond 100 MeV. In the RIT scheme, the absorption of the electromagnetic laser field by the target and the generated plasma is critical. In joint experiments at the DRACO PW (HZDR) and J-KAREN (KPSI) lasers, we use transmission diagnostics to study the onset of transparency and learn about the sensitivity of the laser input to improve the process’s robustness. Using ultra-short pulses on thin solid density foil targets, we observe high performance proton beams in an expanded foil case. Our analysis of the effects on the transmission and its correlation with the acceleration performance indicates changes in the plasma interaction process.

Atomic Population Kinetics for ParticleInCell Standard atomic physics models in PIC simulation either neglect excited states, predict atomic state population in post processing only, or assume quasi-thermal plasma conditions. This is no longer sufficient for high-intensity short-pulse laser generated plasmas, due to their non-equilibrium, transient and non-thermal plasma conditions, which are now becoming accessible in XFEL experiments at HIBEF (EuropeanXFEL), SACLA (Japan) or at MEC (LCLS/SLAC). To remedy this, we have developed a new extension for our PIC simulation framework PIConGPU to allow us to model atomic population kinetics in-situ in PIC-Simulations, in transient plasmas and without assuming any temperatures. This extension is based on a reduced atomic state model, which is directly coupled to the existing PIC-simulation and for which the atomic rate equation is solved explicitly in time, depending on local interaction spectra and with feedback to the host simulation. This allows us to model de-/excitation and ionization of ions in transient plasma conditions, as typically encountered in laser accelerator plasmas. This new approach to atomic physics modeling will be very useful in plasma emission prediction, plasma condition probing with XFELs and better understanding of isochoric heating processes, since all of these rely on an accurate prediction of atomic state populations inside transient plasmas.

The volume of data generated by exascale simulations requires scalable tools for analysis and visualization. Due to the relatively low I/O bandwidth of modern HPC systems, it is crucial to work as close as possible with simulated data via in situ approaches. In situ visualization provides insights into simulation data and, with the help of additional interactive analysis tools, can support the scientific discovery process at an early stage. Such in situ visualization tools need to be hardware-independent given the ever-increasing hardware diversity of modern supercomputers. We present a new in situ 3D vector field visualization algorithm for particle-in-cell (PIC) simulations and performance evaluation of the solution developed at large-scale. We create a solution in a hardware-agnostic approach to support high throughput and interactive in situ processing on leadership class computing systems. To that end, we demonstrate performance portability on Summit’s and the Frontier’s pre-exascale testbed at the Oak Ridge Leadership Computing Facility.

Ion acceleration by laser-plasma sources promises many applications, but reaching the required beam quality parameters demands a high level of understanding and control over the interaction process. Several advanced schemes, including the Relativistically Induced Transparency (RIT) regime, have been proposed and investigated in search of a stable acceleration for proton energies beyond 100 MeV. In the RIT scheme, the absorption of the electromagnetic laser field by the target and the generated plasma is critical. In joint experiments at the DRACO PW (HZDR) and J-KAREN (KPSI) lasers, we use transmission diagnostics to study the onset of transparency and learn about the sensitivity of the laser input to improve the process’s robustness. Using ultra-short pulses on thin solid density foil targets, we observe high performance proton beams in an expanded foil case. Our analysis of the effects on the transmission and its correlation with the acceleration performance indicates changes in the plasma interaction process.

Ion acceleration by compact laser-plasma sources has great potential for a range of applications, including those with medical relevance and fusion experiments. However, in order to achieve the necessary beam quality parameters for these applications, a thorough understanding and control over the laser-plasma interaction process is required. Therefore, we are exploring laser plasma acceleration around the promising regime of Relativistically Induced Transparency (RIT) through joint studies at DRACO PW (HZDR) and the J-KAREN-P laser system at KPSI. We have performed thickness scans to investigate the relation between proton acceleration performance and target transparency, revealing high-performance proton beams (> 60 MeV) in an expanded foil case, with an optimum at the onset of target transparency. [1] Subsequent experiments showed even higher proton energies. Clearly, the relationship between the transparency onset time and the acceleration performance is crucial for achieving optimal beam parameters, improving our understanding of the sensitivity of laser input parameters, and increasing the process’s robustness. Thus, we are using a combination of particle and laser diagnostics to investigate this correlation. In this contribution, we present a summary of our studies in which we use spectral interferometry with the unperturbed laser beam as a reference to evaluate the output of reflected and transmitted light diagnostics. Obtaining features like shown in Bagnoud et al. [2] and Williamson et al. [3], we additionally correlate these measures with the proton acceleration performance and show first results of spectral, spatial, and energy analysis of the effects on the laser transmission through the target. References [1] Dover, N. P. et al.: Enhanced ion acceleration from transparency-driven foils demonstrated at two ultraintense laser facilities. Light Sci. Appl. in press (2023). [2] Bagnoud, V. et al.: Studying the Dynamics of Relativistic Laser-Plasma Interactio

The volume of data generated by exascale simulations requires scalable tools for analysis and visualization. Due to the relatively low I/O bandwidth of modern HPC systems, it is crucial to work as close as possible with simulated data via in situ approaches. In situ visualization provides insights into simulation data and, with the help of additional interactive analysis tools, can support the scientific discovery process at an early stage. Such in situ visualization tools need to be hardware-independent given the ever-increasing hardware diversity of modern supercomputers. We present a new in situ 3D vector field visualization algorithm for particle-in-cell (PIC) simulations and performance evaluation of the solution developed at large-scale. We create a solution in a hardware-agnostic approach to support high throughput and interactive in situ processing on leadership class computing systems. To that end, we demonstrate performance portability on Summit’s and the Frontier’s pre-exascale testbed at the Oak Ridge Leadership Computing Facility.

Recently, free electron lasing at UV wavelength has been demonstrated by deploying the COXINEL beamline driven by HZDR plasma accelerator in a seeded configuration[1]. Further control and optimization of such an FEL radiation require full knowledge of strongly-coupled multivariate parameters involved in laser plasma acceleration, electron beam transport and radiation generation. For this purpose, one has to solve an inverse problem, i.e. finding matching parameters of the simulation to reproduce the experiment. Such inverse problems are ill-posed and cannot be easily resolved due to high computational complexity. Here, machine learning-based methods have a high potential to accelerate theoretical comprehension of the system, novel means for design space exploration and promise reliable in-situ analysis of experimental diagnostics and parameters. We apply simulation-based inference technique for this purpose. This method is a combination of deep learning and statistical approaches to resolve an inverse problem up to a posterior distribution of the simulation parameters given an experimental sample. In addition, we have developed machine learning-based surrogate models that can significantly accelerate forward computations for even faster results of the inverse solver. [1] M. Labat, et al. "Seeded free-electron laser in driven by a compact laser plasma accelerator", Nat. Photonics, 17, 150(2023)

Recently, free electron lasing at UV wavelength has been demonstrated by deploying the COXINEL beamline driven by HZDR plasma accelerator in a seeded configuration[1]. Further control and optimization of such an FEL radiation require full knowledge of strongly-coupled multivariate parameters involved in laser plasma acceleration, electron beam transport and radiation generation. For this purpose, one has to solve an inverse problem, i.e. finding matching parameters of the simulation to reproduce the experiment. Such inverse problems are ill-posed and cannot be easily resolved due to high computational complexity. Here, machine learning-based methods have a high potential to accelerate theoretical comprehension of the system, novel means for design space exploration and promise reliable in-situ analysis of experimental diagnostics and parameters. We apply simulation-based inference technique for this purpose. This method is a combination of deep learning and statistical approaches to resolve an inverse problem up to a posterior distribution of the simulation parameters given an experimental sample. In addition, we have developed machine learning-based surrogate models that can significantly accelerate forward computations for even faster results of the inverse solver. [1] M. Labat, et al. "Seeded free-electron laser in driven by a compact laser plasma accelerator", Nat. Photonics, 17, 150(2023)

Recently, free electron lasing at UV wavelength has been demonstrated by deploying the COXINEL beamline driven by HZDR plasma accelerator in a seeded configuration[1]. Further control and optimization of such an FEL radiation require full knowledge of strongly-coupled multivariate parameters involved in laser plasma acceleration, electron beam transport and radiation generation. For this purpose, one has to solve an inverse problem, i.e. finding matching parameters of the simulation to reproduce the experiment. Such inverse problems are ill-posed and cannot be easily resolved due to high computational complexity. Here, machine learning-based methods have a high potential to accelerate theoretical comprehension of the system, novel means for design space exploration and promise reliable in-situ analysis of experimental diagnostics and parameters. We apply simulation-based inference technique for this purpose. This method is a combination of deep learning and statistical approaches to resolve an inverse problem up to a posterior distribution of the simulation parameters given an experimental sample. In addition, we have developed machine learning-based surrogate models that can significantly accelerate forward computations for even faster results of the inverse solver. [1] M. Labat, et al. "Seeded free-electron laser in driven by a compact laser plasma accelerator", Nat. Photonics, 17, 150(2023)

The output of simulations can be extremely challenging to work with. For example, in large-scale particle-in-cell simulations in plasma physics, trillions of particles are simulated over millions of time steps, creating Petabytes of data. In this project, we develop methods to compress particle data by training conditional invertible neural networks (cINNs) on the particle data. The particles then can be reconstructed by running the trained model in generative mode. This allows us to reach up to millionfold compression, and a controlled loss of a accuracy. The models can be conditioned not only on the temporal axis but also on other types of simulation outputs of smaller data volume, leading potentially to even higher compression factors. In order to enable the neural network model to represent simulation data over long time spans, we apply methods from Continual Learning, where each new learning task is represented by a dataset produced by a simulation time step. This approach enables us to efficiently solve the inverse problem of particle data reconstruction from radiation data in a time-resolved manner by side-stepping demanding simulations.

The combination of laser wakefield acceleration (LWFA) with plasma wakefield acceleration (PWFA) provides a miniaturized testbed for the study of PWFA. Since the first experimental implementation of this hybrid concept, various driver generation methods and witness injection have been investigated. Extensive simulation studies accompanied these experiments and provided insights into the complex interplay between the individual stages and the processes occurring within them. This real-world implementation allowed us to revisit and refine the original concepts. Here we present this revised implementation guide for LPWFA. Derived from these simulation campaigns, we present a theoretical analysis of the processes relevant to LPWFA hybrid accelerators. Requirements and possible methods for generating a driver package in the LWFA stage are discussed, and different laser extraction techniques are compared. The driver evolution in the PWFA stage is studied and the implications for performance limits relevant to current experiments are discussed. Different witness generation methods are also compared in terms of reproducibility and beam quality. The analysis is based on 3D3V particle-in-cell simulations performed with PIConGPU. Its 3D capability and efficiency allows studying non-rotational effects such as oblique density profiles of shocks and higher laser modes originating from experimental measurements. Their influences are briefly discussed, as well as general requirements on start-to-end simulations.

Atomic Population Kinetics for ParticleInCell Standard atomic physics models in PIC simulation either neglect excited states, predict atomic state population in post processing only, or assume quasi-thermal plasma conditions. This is no longer sufficient for high-intensity short-pulse laser generated plasmas, due to their non-equilibrium, transient and non-thermal plasma conditions, which are now becoming accessible in XFEL experiments at HIBEF (EuropeanXFEL), SACLA (Japan) or at MEC (LCLS/SLAC). To remedy this, we have developed a new extension for our PIC simulation framework PIConGPU to allow us to model atomic population kinetics in-situ in PIC-Simulations, in transient plasmas and without assuming any temperatures. This extension is based on a reduced atomic state model, which is directly coupled to the existing PIC-simulation and for which the atomic rate equation is solved explicitly in time, depending on local interaction spectra and with feedback to the host simulation. This allows us to model de-/excitation and ionization of ions in transient plasma conditions, as typically encountered in laser accelerator plasmas. This new approach to atomic physics modeling will be very useful in plasma emission prediction, plasma condition probing with XFELs and better understanding of isochoric heating processes, since all of these rely on an accurate prediction of atomic state populations inside transient plasmas.

The Draco laboratory at HZDR is a versatile, multi-arm and multi-target-area facility, consisting of several, independent subsystems. The lack of an overarching DAQ is balanced by interfaces of the subsystems and custom inter-linking agents. We present recent progress of implementing such software agents, connecting to the center’s electronic lab documentation system. First, manual logging of shot parameters and observations is lifted from spreadsheet software to a flexible web-app, writing to a database (DB). The laser-internal logging is exported to a DB and internal software triggering is forwarded to experiments. That provides a connection between laser-internal indexing and experiment-based indexing (another DB) and enables near-online data processing. The latter comprises file path logging and validation according to the shot’s acquisition settings for further analysis as well as basic on-shot analysis scripts, both enabling near-online visualization to better guide the course of experiments. Likewise, parameters and results from simulations are logged to databases, enabling machine learning techniques and better computing resource management. For a long-term, FAIR storage, the HELPMI project starts exploring the possibilities of openPMD and NeXus to ingest experimental data. That project shall serve as initiative for the global LPA community to find a data and metadata standard.

Recently, free electron lasing at UV wavelength has been demonstrated by deploying the COXINEL beamline driven by HZDR plasma accelerator in a seeded configuration[1]. Further control and optimization of such an FEL radiation require full knowledge of strongly-coupled multivariate parameters involved in laser plasma acceleration, electron beam transport and radiation generation. For this purpose, one has to solve an inverse problem, i.e. finding matching parameters of the simulation to reproduce the experiment. Such inverse problems are ill-posed and cannot be easily resolved due to high computational complexity. Here, machine learning-based methods have a high potential to accelerate theoretical comprehension of the system, novel means for design space exploration and promise reliable in-situ analysis of experimental diagnostics and parameters. We apply simulation-based inference technique for this purpose. This method is a combination of deep learning and statistical approaches to resolve an inverse problem up to a posterior distribution of the simulation parameters given an experimental sample. In addition, we have developed machine learning-based surrogate models that can significantly accelerate forward computations for even faster results of the inverse solver. [1] M. Labat, et al. "Seeded free-electron laser in driven by a compact laser plasma accelerator", Nat. Photonics, 17, 150(2023)

HELPMI is a 2-year project, subsidized by the Helmholtz Metadata Collaboration, conducted by GSI, HI Jena and HZDR (lead). The aim is to start the development of a F.A.I.R. data standard for experimental data of the entire laser-plasma (LPA) community. Such standard does not yet exist. It will facilitate management and analysis of usually quite heterogeneous experimental data and logs by rich and machine-actionable metadata, allowing automated processing of broad and long data sets. To date, the LPA community is widely using openPMD, an open meta-standard, well-established for simulations. NeXus is a similarly hierarchical and extensible standard for various experimental methods of the Photon and Neutron science community. Within HELPMI, we plan to adopt NeXus for LPA experimental data and simultaneously to make openPMD and its API extensible for custom hierarchies like NeXus. Thereby we can achieve interoperability of the standards, circumventing the need for another standard. Alongside we will start developing a glossary of LPA experimental terms in order to achieve re-usability. The glossary shall be community-driven and technically open, extensible and implementation-independent.

HELPMI is a 2-year project, subsidized by the Helmholtz Metadata Collaboration, conducted by GSI, HI Jena and HZDR (lead). The aim is to start the development of a F.A.I.R. data standard for experimental data of the entire laser-plasma (LPA) community. Such standard does not yet exist. It will facilitate management and analysis of usually quite heterogeneous experimental data and logs by rich and machine-actionable metadata, allowing automated processing of broad and long data sets. To date, the LPA community is widely using openPMD, an open meta-standard, well-established for simulations. NeXus is a similarly hierarchical and extensible standard for various experimental methods of the Photon and Neutron science community. Within HELPMI, we plan to adopt NeXus for LPA experimental data and simultaneously to make openPMD and its API extensible for custom hierarchies like NeXus. Thereby we can achieve interoperability of the standards, circumventing the need for another standard. Alongside we will start developing a glossary of LPA experimental terms in order to achieve re-usability. The glossary shall be community-driven and technically open, extensible and implementation-independent.

Particle-in-cell (PIC) simulations are a widely-used tool to model kinetics-dominated plasmas in ultrarelativistic laser-solid interactions (dimensionless vectorpotential a0 > 1). However, interactions approaching subrelativistic laser intensities (a0 ≲ 1) are governed by correlated and collisional plasma physics, calling for benchmarks of available modeling capabilities and the establishment of standardized testbeds. Here, we propose such a testbed to experimentally benchmark PIC simulations of laser-solid interactions using a laser-irradiated micron-sized cryogenic hydrogen-jet target. Time-resolved optical shadowgraphy of the expanding plasma density, complemented by hydrodynamics and ray-tracing simulations, is used to determine the bulk-electron-temperature evolution after laser irradiation. We showcase our testbed by studying isochoric heating of solid hydrogen induced by laser pulses with a dimensionless vectorpotential of a0 ≈ 1. Our testbed reveals that the initial surface-density gradient of the target is decisive to reach quantitative agreement at 1 ps after the interaction, demonstrating its suitability to benchmark controlled parameter scans at subrelativistic laser intensities.

We report on electron beam collimation using a passive plasma lens, integrated directly and conveniently into a laser wakefield accelerator stage operating in the high charge regime. The lens is created by reshaping the gas density profile of a super-sonic jet at the beam's exit side through an obstacle mounted above the jet. It reduces the beam's divergence by a factor of two to below 1 mrad (root-mean-square), while preserving the total charge of 170 pC and maintaining the energy spread. The resulting spectral-charge density, here defined as the charge per energy bandwidth and emission angle, of up to 7 pC/(MeV·mrad) played a key role in the recent experimental demonstration of free-electron lasing. The presented simple and robust gas shaping technique holds the potential to generate specific density profiles, essential for the application of adiabatic focusing or staging of accelerators.

Advancing laser accelerated proton beam performance for dose controlled irradiation studies and beyond the 100 MeV frontier

Advancing laser accelerated proton beam performance for dose controlled irradiation studies and beyond the 100 MeV frontier

Talk on: Ion acceleration with ultrashort laser pulses: state-of-the-art and perspectives in the multi-PW regime

Presentation summing up the work of the Helmholtz task force AG Laser - the fusion energy options: Fusion related laser plasma expertise and interests in the German Helmholtz Association

We present a method extending scanning third-order correlator temporal pulse14 evolution measurement capabilities of high power short pulse lasers to spectral sensitivity within15 the spectral range exploited by typical chirped pulse amplification systems. Modelling of the16 spectral response achieved by angle tuning of the third harmonic generating crystal is applied17 and experimentally validated. Exemplary measurements of spectrally resolved pulse contrast of a18 Petawatt laser frontend illustrate the importance of full bandwidth coverage for the interpretation19 of relativistic laser target interaction in particular for the case of solid targets.

Laser plasma-based proton accelerators (LPA) can contribute to research of ultra-high dose rate radiobiology as they provide pulse dose rates unprecedented at medical proton sources. Yet, LPAs pose challenges regarding precise dosimetry due to the high pulse dose rates, but also due to the sources' lower spectral stability and pulsed operation mode. For in-vivo models, further challenges arise from the necessary small field dosimetry for volumetric dose distributions. In this work, we present a dosimetry and beam monitoring concept for in-vivo irradiations of small target volumes with LPA protons, solving aforementioned challenges. The volumetric dose distribution in a sample (mean dose value and lateral/depth dose inhomogeneity) is provided by combining two independent dose measurements using radiochromic films (dose-rate independent) and ionization chambers (dose-rate dependent), respectively. The unique feature of the dosimetric setup is beam monitoring with a transmission time-of-flight spectrometer to quantify spectral fluctuations of the irradiating proton pulses. The resulting changes in the depth dose profile during irradiation of an in-vivo sample are hence accessible and enable pulse-resolved depth dose correction for each dose measurement. A first successful small animal pilot study using an LPA proton source serves as a testcase for the presented dosimetry approach and proves its performance in a realistic setting.

The recently observed FLASH effect provides normal tissue protection at a similar tumor treatment efficacy via ultra-high dose rate (UHDR) irradiation and promises great benefits for radiotherapy patients. Dedicated studies are now necessary to define a robust set of dose application parameters for FLASH radiotherapy and to identify underlying mechanisms. These studies require particle accelerators with variable temporal dose application characteristics for numerous radiation qualities, equipped for preclinical radiobiological research. Here we present the DRESDEN PLATFORM, a research hub for ultra-high dose rate radiobiology. By uniting clinical and research accelerators with radiobiology infrastructure and know-how, the DRESDEN PLATFORM offers a unique environment for studying the FLASH effect. We introduce its experimental capabilities and qualify the platform for systematic investigation of FLASH by presenting results from a concerted in vivo radiobiology study with zebrafish embryos. The comparative pre-clinical study was conducted across three accelerator facilities, including an advanced laser-driven proton source applied for FLASH-relevant in vivo irradiations for the first time. The data show a protective effect of UHDR irradiation up to 10^5 Gy/s and suggests consistency of the protective effect even at escalated dose rates of 10^9 Gy/s. With the first clinical FLASH studies underway, research facilities like the DRESDEN PLATFORM, addressing the open questions surrounding FLASH, are essential to accelerate FLASH’s translation into clinical practice.

The coherent infrared and THz sources driven by the superconducting electron accelerator ELBE are described. The present status of the facility is summarized and a few scientific highlights are mentioned. Finally plans for a successor facility (Dresden Advanced Light Infrastructure, DALI) are outlined along with the most important scientific and technological challenges.

Laser-plasma accelerated (LPA) proton bunches are now prepared and provided for research fields ranging from ultra-high dose rate radiobiology to material science and probing of extreme states of matter. Yet, the capabilities to fully characterize the spectrally and angularly broad LPA bunches lag behind the rapidly evolving applications. The Optical Cone beam TOmograph for Proton Online Dosimetry - short OCTOPOD - translates the angularly resolved spectral characterization of LPA proton bunches into the spatially resolved detection of the volumetric dose distribution deposited in a liquid scintillator. Up to 24 multi-pinhole arrays record projections of the volumetric scintillation light distribution and allow for tomographic reconstruction of the volumetric dose deposition pattern, from which proton spectra may be retrieved in arbitrary directions by spectral deconvolution. Applying the OCTOPOD at a cyclotron, we show the reliable retrieval of various spatial dose deposition patterns and detector sensitivity over a broad dose range from less than 1 Gy to more than 100 Gy, as required for LPA proton bunch characterization. With a dedicated vacuum housing, the OCTOPOD was installed at a LPA proton source, providing real-time data on proton acceleration performance for various laser-target interaction parameters and attesting the system optimal performance in the harsh laser plasma environment.

Plasma wakefield accelerators can be driven either by intense laser pulses (LWFA) or by intense particle beams (PWFA). A third approach that combines the complementary advantages of both types of plasma wakefield accelerator has been established with increasing success over the last decade and is called hybrid LWFA→PWFA. Essentially, a compact LWFA is exploited to produce an energetic, high-current electron beam as a driver for a subsequent PWFA stage, which, in turn, is exploited for phase-constant, inherently laser-synchronized, quasi-static acceleration over extended acceleration lengths. The sum is greater than its parts: the approach not only provides a compact, cost-effective alternative to linac-driven PWFA for exploitation of PWFA and its advantages for acceleration and high-brightness beam generation, but extends the parameter range accessible for PWFA and, through the added benefit of co-location of inherently synchronized laser pulses, enables high-precision pump/probing, injection, seeding and unique experimental constellations, e.g., for beam coordination and collision experiments. We report on the accelerating progress of the approach achieved in a series of collaborative experiments and discuss future prospects and potential impact.

Article about Heating in multi-layered target upon UHI laser irradiation

Laser plasma-based particle accelerators attract great interest in fields where conventional accelerators reach limits based on size, cost or beam parameters. Despite the fact that particle in cell simulations have predicted several advantageous ion acceleration schemes, laser accelerators have not yet reached their full potential in producing simultaneous high-radiation doses at high particle energies. The most stringent limitation is the lack of a suitable high-repetition rate target that also provides a high degree of control of the plasma conditions required to access these advanced regimes. Here, we demonstrate that the interaction of petawatt-class laser pulses with a pre-formed micrometer-sized cryogenic hydrogen jet plasma overcomes these limitations enabling tailored density scans from the solid to the underdense regime. Our proof-of-concept experiment demonstrates that the near-critical plasma density profile produces proton energies of up to 80 MeV. Based on hydrodynamic and three-dimensional particle in cell simulations, transition between different acceleration schemes are shown, suggesting enhanced proton acceleration at the relativistic transparency front for the optimal case.

We present EZ, a novel Current Deposition algorithm for particle-in-cell simulations, which calculates the current density field due to macro-particle motion within a time step by solving the electrodynamic continuity equation. Being a charge conserving hybridization of \textbf{E}sirkepov's method and \textbf{Z}igZag, we refer to it as ``EZ'' as shorthand for ``Esirkepov meets ZigZag''. EZ achieves the same level of charge conservation as the commonly used method by Esirkepov, yet reaches higher performance for macro-particle assignment-functions up to third-order. Key considerations of its implementation in PIConGPU, an open source, C++, performance portable, fully relativistic 3D3V particle-in-cell code, are outlined in addition to the detailed description of EZ along with remarks on its optimization and customization.

We report on the development of an ultrafast optical parametric amplifier (uOPA) front-end for the Petawatt High Energy Laser for heavy Ion eXperiments (PHELIX) and the Petawatt ENergy-Efficient Laser for Optical Plasma Experiments (PE NELOPE). This front-end delivers broadband and stable amplification up to 1 mJ per pulse while maintaining a high beam quality. Its implementation at PHELIX allowed to bypass a front-end amplifier that was known to be a source of pre-pulses. With the bypass, an amplified spontaneous emission (ASE) contrast of 4.9·10−13 and a pre-pulse c ntrast of 6.2·10−11 could be realized. Due to its high stability, high beam quality and its versatile pump amplifier, the system offers an alternative for high-gain regenerative amplifiers in the front-end of vario

The acoustic pulse emitted from the Bragg peak of a laser-accelerated proton bunch focused into water has recently enabled the reconstruction of the bunch energy distribution. By adding three ultrasonic transducers and implementing a fast data analysis of the filtered raw signals, I-BEAT (Ion-Bunch Energy Acoustic Tracing) 3D now provides the mean bunch energy and absolute lateral bunch position in real-time and for individual bunches. Relative changes in energy spread and lateral bunch size can also be monitored. Our experiments at DRACO with proton bunch energies between 10 and 30 MeV reveal sub-MeV and sub-mm resolution. In addition to this 3D bunch information, the signal strength correlates also with the absolute bunch particle number.

For high-intensity laser-solid interactions, the absolute density and surface density gradients of the target at the arrival of the ultra-relativistic laser peak are critical parameters. Accurate modeling of the leading edge-driven target pre-expansion is desired to strengthen the predictive power of associated computer simulations. The transition from an initial solid state to a plasma state, i.e., the breakdown of the solid, defines the starting point of the subsequent target pre-expansion. In this work, we report on the time-resolved observation of transient laser-induced breakdown (LIB) during the leading edge of high-intensity petawatt-class laser pulses with peak intensities of up to 5.7 × 10^21 W/cm^2 in interaction with dielectric cryogenic hydrogen jet targets. LIB occurs much earlier than what is typically expected following the concept of barrier suppression ionization. The observation is explained by comparing a characterization study of target specific LIB thresholds with laser contrast measurements. The results demonstrate the relevance of the laser pulse duration dependence of LIB for high-intensity laser-solid interactions. We provide an effective approach to determine the onset of LIB and thereby the starting point of target pre-expansion in other laser-target systems.

Seeded free-electron laser driven by a compact laser plasma accelerator Free-electron lasers generate high-brilliance coherent radiation at wavelengths spanning from the infrared to the X-ray domains. The recent development of short-wavelength seeded free-electron lasers now allows for unprecedented levels of control on longitudinal coherence, opening new scientific avenues such as ultra-fast dynamics on complex systems and X-ray nonlinear optics. Although those devices rely on state-of-the-art large-scale accelerators, advancements on laser-plasma accelerators, which harness gigavolt-per-centimetre accelerating fields, showcase a promising technology as compact drivers for free-electron lasers. Using such footprint-reduced accelerators, exponential amplification of a shot-noise type of radiation in a self-amplified spontaneous emission configuration was recently achieved. However, employing this compact approach for the delivery of temporally coherent pulses in a controlled manner has remained a major challenge. Here we present the experimental demonstration of a laser-plasma accelerator-driven free-electron laser in a seeded configuration, where control over the radiation wavelength is accomplished. Furthermore, the appearance of interference fringes, resulting from the interaction between the phase-locked emitted radiation and the seed, confirms longitudinal coherence. Building on our scientific achievements, we anticipate a navigable pathway to extreme-ultraviolet wavelengths, paving the way towards smaller-scale free-electron lasers, unique tools for a multitude of applications in industry, laboratories and universities.

The volume of data generated by exascale simulations requires scalable tools for analysis and visualization. Due to the relatively low I/O bandwidth of modern HPC systems, it is crucial to work as close as possible with simulated data via in situ approaches. In situ visualization provides insights into simulation data and, with the help of additional interactive analysis tools, can support the scientific discovery process at an early stage. Such in situ visualization tools need to be hardware-independent given the ever-increasing hardware diversity of modern supercomputers. We present a new in situ 3D vector field visualization algorithm for particle-in-cell (PIC) simulations and performance evaluation of the solution developed at large-scale. We create a solution in a hardware-agnostic approach to support high throughput and interactive in situ processing on leadership class computing systems. To that end, we demonstrate performance portability on Summit’s and the Frontier’s pre-exascale testbed at the Oak Ridge Leadership Computing Facility.

Recently, free electron lasing at UV wavelength has been demonstrated by deploying the COXINEL beamline driven by HZDR plasma accelerator in a seeded configuration[1]. Further control and optimization of such an FEL radiation require full knowledge of strongly-coupled multivariate parameters involved in laser plasma acceleration, electron beam transport and radiation generation. For this purpose, one has to solve an inverse problem, i.e. finding matching parameters of the simulation to reproduce the experiment. Such inverse problems are ill-posed and cannot be easily resolved due to high computational complexity. Here, machine learning-based methods have a high potential to accelerate theoretical comprehension of the system, novel means for design space exploration and promise reliable in-situ analysis of experimental diagnostics and parameters. We apply simulation-based inference technique for this purpose. This method is a combination of deep learning and statistical approaches to resolve an inverse problem up to a posterior distribution of the simulation parameters given an experimental sample. In addition, we have developed machine learning-based surrogate models that can significantly accelerate forward computations for even faster results of the inverse solver. [1] M. Labat, et al. "Seeded free-electron laser in driven by a compact laser plasma accelerator", Nat. Photonics, 17, 150(2023)

The combination of laser wakefield acceleration (LWFA) with plasma wakefield acceleration (PWFA) provides a miniaturized testbed for the study of PWFA. Since the first experimental implementation of this hybrid concept, various driver generation methods and witness injection have been investigated. Extensive simulation studies accompanied these experiments and provided insights into the complex interplay between the individual stages and the processes occurring within them. This real-world implementation allowed us to revisit and refine the original concepts. Here we present this revised implementation guide for LPWFA. Derived from these simulation campaigns, we present a theoretical analysis of the processes relevant to LPWFA hybrid accelerators. Requirements and possible methods for generating a driver package in the LWFA stage are discussed, and different laser extraction techniques are compared. The driver evolution in the PWFA stage is studied and the implications for performance limits relevant to current experiments are discussed. Different witness generation methods are also compared in terms of reproducibility and beam quality. The analysis is based on 3D3V particle-in-cell simulations performed with PIConGPU. Its 3D capability and efficiency allows studying non-rotational effects such as oblique density profiles of shocks and higher laser modes originating from experimental measurements. Their influences are briefly discussed, as well as general requirements on start-to-end simulations.

Ion acceleration by compact laser-plasma sources has great potential for a range of applications, including those with medical relevance and fusion experiments. However, in order to achieve the necessary beam quality parameters for these applications, a thorough understanding and control over the laser-plasma interaction process is required. Therefore, we are exploring laser plasma acceleration around the promising regime of Relativistically Induced Transparency (RIT) through joint studies at DRACO PW (HZDR) and the J-KAREN-P laser system at KPSI. We have performed thickness scans to investigate the relation between proton acceleration performance and target transparency, revealing high-performance proton beams (> 60 MeV) in an expanded foil case, with an optimum at the onset of target transparency. [1] Subsequent experiments showed even higher proton energies. Clearly, the relationship between the transparency onset time and the acceleration performance is crucial for achieving optimal beam parameters, improving our understanding of the sensitivity of laser input parameters, and increasing the process’s robustness. Thus, we are using a combination of particle and laser diagnostics to investigate this correlation. In this contribution, we present a summary of our studies in which we use spectral interferometry with the unperturbed laser beam as a reference to evaluate the output of reflected and transmitted light diagnostics. Obtaining features like shown in Bagnoud et al. [2] and Williamson et al. [3], we additionally correlate these measures with the proton acceleration performance and show first results of spectral, spatial, and energy analysis of the effects on the laser transmission through the target. References [1] Dover, N. P. et al.: Enhanced ion acceleration from transparency-driven foils demonstrated at two ultraintense laser facilities. Light Sci. Appl. in press (2023). [2] Bagnoud, V. et al.: Studying the Dynamics of Relativistic Laser-Plasma Interactio

Ion acceleration by laser-plasma sources promises many applications, but reaching the required beam quality parameters demands a high level of understanding and control over the interaction process. Several advanced schemes, including the Relativistically Induced Transparency (RIT) regime, have been proposed and investigated in search of a stable acceleration for proton energies beyond 100 MeV. In the RIT scheme, the absorption of the electromagnetic laser field by the target and the generated plasma is critical. In joint experiments at the DRACO PW (HZDR) and J-KAREN (KPSI) lasers, we use transmission diagnostics to study the onset of transparency and learn about the sensitivity of the laser input to improve the process’s robustness. Using ultra-short pulses on thin solid density foil targets, we observe high performance proton beams in an expanded foil case. Our analysis of the effects on the transmission and its correlation with the acceleration performance indicates changes in the plasma interaction process.